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Papers/A Twofold Siamese Network for Real-Time Object Tracking

A Twofold Siamese Network for Real-Time Object Tracking

Anfeng He, Chong Luo, Xinmei Tian, Wen-Jun Zeng

2018-02-24CVPR 2018 6Image ClassificationObject Tracking
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Abstract

Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similarity-learning Siamese network. An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC \cite{SiamFC} allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks.

Results

TaskDatasetMetricValueModel
Object TrackingOTB-50AUC0.61SA-Siam
Object TrackingOTB-2013AUC0.677SA-Siam
Object TrackingOTB-2015AUC0.657SA-Siam
Visual Object TrackingOTB-50AUC0.61SA-Siam
Visual Object TrackingOTB-2013AUC0.677SA-Siam
Visual Object TrackingOTB-2015AUC0.657SA-Siam

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